Modeling of 2D+1 texture movies for video coding

We propose a novel model-based coding system for video. Model-based coding aims at improving compression gain by replacing the non-informative image elements with some perceptually equivalent models. Images enclosing large textured regions are ideal candidates. Texture movies are obtained by filming a static texture with a moving camera. The integration of the motion information within the generative texture process allows to replace the “real” texture with a “visually equivalent” synthetic one, while preserving the correct motion perception. Global motion estimation is used to determine the movement of the camera and to identify the overlapping region between two successive frames. Such an information is then exploited for the generation of the texture movies. The proposed method for synthesizing 2D+1 texture movies is able to emulate any piece-wise linear trajectory. Compression performances are very encouraging. On this kind of video sequences, the proposed method improves the compression rate of an MPEG4 state-of-the-art video coder of an order of magnitude while providing a sensibly better perceptual quality. Importantly, the current implementation is real-time on Intel PIII processors.

[1]  Stefano Soatto,et al.  Dynamic Textures , 2003, International Journal of Computer Vision.

[2]  Song-Chun Zhu,et al.  FRAME: filters, random fields, and minimax entropy towards a unified theory for texture modeling , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Paul A. Viola,et al.  A Non-Parametric Multi-Scale Statistical Model for Natural Images , 1997, NIPS.

[4]  Martin Szummer,et al.  Temporal texture modeling , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[5]  J. Preston Ξ-filters , 1983 .

[6]  Alexei A. Efros,et al.  Texture synthesis by non-parametric sampling , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[7]  Eero P. Simoncelli,et al.  Natural image statistics and neural representation. , 2001, Annual review of neuroscience.

[8]  I. Daubechies,et al.  Wavelet Transforms That Map Integers to Integers , 1998 .

[9]  Marc Levoy,et al.  Fast texture synthesis using tree-structured vector quantization , 2000, SIGGRAPH.

[10]  Baining Guo,et al.  Chaos Mosaic: Fast and Memory Efficient Texture Synthesis , 2000 .

[11]  M. Landy Texture perception , 1996 .

[12]  M S Landy,et al.  Ideal cue combination for localizing texture-defined edges. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[13]  Til Aach,et al.  Statistical model-based change detection in moving video , 1993, Signal Process..

[14]  Michael Ashikhmin,et al.  Synthesizing natural textures , 2001, I3D '01.

[15]  I. Daubechies,et al.  Factoring wavelet transforms into lifting steps , 1998 .

[16]  Gloria Menegaz DWT based non-parametric texture modeling , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[17]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[18]  Dani Lischinski,et al.  Texture Mixing and Texture Movie Synthesis Using Statistical Learning , 2001, IEEE Trans. Vis. Comput. Graph..

[19]  S. S. Wolfson,et al.  PII: S0042-6989(97)00153-3 , 2003 .

[20]  Julien Reichel Complexity related aspects of image compression , 2002 .

[21]  James R. Bergen,et al.  Pyramid-based texture analysis/synthesis , 1995, Proceedings., International Conference on Image Processing.

[22]  Song-Chun Zhu,et al.  Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling , 1998, International Journal of Computer Vision.

[23]  Zhaoping Li A saliency map in primary visual cortex , 2002, Trends in Cognitive Sciences.

[24]  Eero P. Simoncelli,et al.  A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients , 2000, International Journal of Computer Vision.

[25]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[26]  M. Kunt,et al.  Second-generation image-coding techniques , 1985, Proceedings of the IEEE.

[27]  Jeremy S. De Bonet,et al.  Multiresolution sampling procedure for analysis and synthesis of texture images , 1997, SIGGRAPH.